Skip to main content
Download PDF

Genomic landscape, immune characteristics and prognostic mutation signature of cervical cancer in China

BMC Medical Genomics volume 15, Article number: 231 (2022) Cite this article

Abstract

Purpose

This study aimed to analyse the genomic alteration profiles and immune characteristics of a cohort of Chinese cervical cancer patients to understand why certain patients benefited from molecular targeted therapies and immunotherapy as well as their prognostic significance.

Methods

PD-L1 expression and clinicopathological information were obtained from 98 cervical cancer patients. Differences in PD-L1 expression and gene mutations between squamous cell carcinoma (SCC) and adenocarcinoma (AC) were analysed by the chi-square test or Fisher's exact test. Differences in gene mutations between our cohort and The Cancer Genome Atlas (TCGA) cohort were tested by Fisher's exact test. Logistic regression was used to analyse factors influencing TMB-high.

Results

Positive PD-L1 expression was significantly higher in cervical SCC than in cervical AC (87% vs. 39%, p < 0.001). Frequently mutated genes in cervical cancer included the PIK3CA, KMT2D, and KMT2C genes, among others. PIK3CA gene mutation rates were significantly higher in SCC than in AC (p = 0.004). The TERT gene mutation rate was significantly higher in our cohort than in the TCGA cohort (12% vs. 1%, p < 0.001). The independent predictors of high TMB were KMT2C and LRP1B gene mutations (p < 0.05). We also found that PTEN mutations were associated with worse survival (median PFS, 12.16 vs. 21.75 months, p = 0.0024).

Conclusion

Cervical SCC and AC have different molecular profiles and immune characteristics, suggesting that targeted treatments for SCC and AC patients may improve clinical outcomes. KMT2C and LRP1B gene mutations are independent predictors of TMB-high status in cervical cancer. We also proposed the prognostic value of PTEN mutations.

Peer Review reports

Background

Cervical cancer is the fourth most common cancer and has a high mortality rate among women worldwide. In China, cervical carcinoma is the second most common malignancy among women [1, 2]. Early cervical cancer can be effectively treated by surgery, while concurrent chemoradiation is the first choice for locally advanced cervical cancer. However, 20–30% of cervical cancer patients experience recurrence or distant metastasis after first-line treatment, and the treatment strategies for these patients are limited. Therefore, local recurrence and distant metastasis are the major causes of death in advanced cervical carcinoma.

In recent years, molecularly targeted therapies have been increasingly applied to various malignancies. The comprehensive genetic and molecular characteristics of malignant cervical tumours were described by The Cancer Genome Atlas Research Network [3]. Nevertheless, there were only 20 Asians in the TCGA cohort, and the TCGA data only published the sequencing results, without analysing the differences between adenocarcinoma and squamous cell carcinoma. In China, only a few studies with small sample sizes or sequencing of a small panel of genes have evaluated gene mutations in cervical cancer. Therefore, it is necessary to establish a larger genetic profile of the Chinese population through next-generation sequencing (NGS).

Immunotherapy is an emerging treatment for cervical cancer. However, the KEYNOTE-158 trial showed that the overall response rate of cervical cancer patients treated with immunotherapy was only 12.2% [4]. Malignant tumour patients with positive PD-L1 expression and high tumour mutational burden (TMB) were considered a patient population that may achieve a sustained benefit from pembrolizumab [5]. Therefore, it is critical to distinguish cervical cancer patients who are more likely to benefit from immune checkpoint inhibitors. However, only a few studies with small samples have reported PD-L1 expression and TMB in Chinese populations with cervical cancer [6, 7].

Squamous cell carcinoma (SCC) and adenocarcinoma (AC) are the prime pathological classifications of cervical carcinoma. Some studies have shown that AC has higher rates of lymph node involvement and distant metastasis than SCC, and the prognosis of AC is poor [8,9,10]. It was reported in a study subgroup analysis that SCC patients have a better response to immunotherapy than AC patients [11]. Only a few studies have investigated whether SCC and AC have different molecular profiles among Chinese patients.

The incidence and spectrum of somatic mutations in carcinoma of the cervix were analysed in the current study. We compared the differences in the molecular profiles, PD-L1 expression, and TMB between cervical SCC and AC in a Chinese population. These findings may help guide the targeted therapy of cervical cancer and lead to the identification of new markers that can predict the therapeutic effect of immune checkpoint inhibitors in cervical cancer patients by integrating the expression of PD-L1, genomic variation and TMB.

Methods

Samples and clinical data

We enrolled 98 patients with cervical cancer in Fujian Cancer Hospital. Clinical data were extracted from electronic medical records. Samples including tumour tissues as well as matched neighbouring normal tissues or peripheral blood were collected.

Immunohistochemistry

Immunohistochemical analysis of PD-L1 expression was performed on 4–5 μm formalin-fixed and paraffin-embedded sections using an anti-human PD-L1 antibody (Dako 22C3) according to the manufacturer’s recommendations. The combined positive score (CPS) was used to measure PD-L1 protein expression. The number of PD-L1-stained cells was divided by the total quantity of surviving tumour cells and then multiplied by 100. Based on previous studies [11], we set two cut-off values of CPS for PD-L1 expression: 1 and 10. PD-L1 positivity was defined as CPS ≥ 1.

Library construction and sequencing [12]

A total amount of 1.0 μg genomic DNA per sample was used as input material for the DNA sample preparation. Sequencing libraries were generated using a customized panel that targeted 484 cancer genes. Briefly, fragmentation was carried out by a hydrodynamic shearing system (Covaris, Massachusetts, USA) to generate 180–280 bp fragments. The remaining overhangs were converted into blunt ends via exonuclease/polymerase activities, and enzymes were removed. After adenylation of the 3’ ends of DNA fragments, adapter oligonucleotides were ligated. DNA fragments with ligated adapter molecules on both ends were selectively enriched in a PCR. Captured libraries were enriched in a PCR to add index tags to prepare for hybridization. The products were purified using an AMPure XP system (Beckman Coulter, Beverly, USA) and quantified using an Agilent high-sensitivity DNA assay on an Agilent Bioanalyzer 2100 system. Libraries were sequenced with standard 2 × 150-bp paired-end reads on the Illumina NovaSeq 6000 sequencer. Sequencing was performed with 1000×–2000×  depth on targeted 3 Mb regions.

Genome alignment and variant calling

After removing sequencing reads with low quality and adapter bases using FASTP (https://github.com/OpenGene/fastp), clean reads were aligned to the human genome (National Center for Biotechnology Information build 37, hg19) using the Burrows–Wheeler Aligner (https://github.com/lh3/bwa). Sorted BAM files were created using sambamba (https://github.com/biod/sambamba). Duplicate reads were marked using samblaster (https://github.com/GregoryFaust/samblaster). Single nucleotide variants (SNVs) and small insertions and deletions (indels) were called and identified using Varscan2 (http://varscan.sourceforge.net). False-positive mutations were removed using in-house filter tools and IGV (http://www.igv.org) double-checked manually with variant allele frequency ≥ 1%.

Functional annotation

We used ANNOVAR (https://annovar.openbioinformatics.org/en/latest/) to annotate the variant call format. Consensus Coding Sequence and RefSeq were used to determine amino acid variations. The annotation content contained variant positions, variant types, and other information. The dbSNP (http://www.bioinfo.org.cn/relative/dbSNP%20Home%20Page.htm), COSMICV91 (https://cancer.sanger.ac.uk/cosmic), 1000 Genomes Project (https://www.internationalgenome.org), and Exome Aggregation Consortium (http://exac.broadinstitute.org) databases were also used to obtain the population frequencies and other information for the mutations.

Open-source dataset acquisition and preprocessing

The MAF files and clinical information of cervical cancer patients were obtained from the TCGA database (https://portal.gdc.cancer.gov). To remove the interbatch effect (TCGA uses whole-exome sequencing (WES) with middle depth, while this project used the Novogene PM2.0 panel with high depth), we extracted mutations of pm2.0-covered genes in the TCGA data using Python 2.7 (Additional file 1: Table S1).

Visualization and data statistics

We used the R package maftools (http://bioconductor.org/packages/release/bioc/vignettes/maftools/inst/doc/maftools.html) for statistical analysis and visualization, including landscape analyses, statistical testing, and other analyses.

Identification of significantly mutated genes

MuSiC package was performed to identify frequently mutated genes that show a significantly higher mutation rate than the background mutation rate (BMR). BMR was calculated as the mutations per Mb in the coding and splicing regions of 484 genes and also in the categories of AT/CG/CpG transitions, AT/CG/CpG transversions, and Indels. MuSiC applied three SMG tests, which include a convolution test (CT), a Fisher’s combined p value test (FCPT) and a likelihood ratio test (LRT), to summarize p values of mutational significance. Genes were considered to be SMGs with FDR < 0.05 in any of the three tests.

Calculation of TMB

The TMB of cervical cancer patients was calculated as the ratio of the total amount of nonsynonymous mutations to the total coding size of the panel (which was 1.2 Mb for the Novogene PM2.0 483 gene panel). The mutation count included nonsynonymous SNVs and indels detected within the coding region, with the exclusion of driver gene events (in the EGFR, MET, BRAF, PIK3CA, NF1, KRAS, TP53, NOTCH1, NOTCH2, NOTCH3, and NOTCH4 genes). Only mutations with allelic fractions of ≥ 2.5% for SNVs and 1.3% for indels were included in the mutation count. TMB cutoff value was defined based on pan-cancer datasets (191 colorectal cancer cases, 297 lung cancer cases, and 326 other cancer cases) which were sequenced with the same panel. TMB was divided into high or low with the top quartile as the cut-off value (5.19 muts/MB).

Statistical analysis

Statistical analyses were conducted using GraphPad PRISM (version 8.0.2) and SPSS (version 25.0). Mutation information analysis was performed using R software (version 4.0.1). The differences in PD-L1 expression and gene mutations between SCC and AC were analysed by the chi-square test or Fisher's exact test. The Benjamini–Hochberg method was used to adjust P values for all the tests when multiple testing was concerned. Differences in mutations between our cohort and the TCGA cohort were tested by Fisher's exact test. The relationship between gene mutations and TMB was analysed by the Mann–Whitney U test. Factors influencing TMB-high were analysed by logistic regression. Progression-free survival (PFS) curves were plotted by the Kaplan–Meier method, and the difference in survival between the two groups was analysed by the log-rank test. Univariate and multivariate Cox regression analyses were conducted to test whether PTEN mutation was an independent prognostic factor. For all analyses, clinical stages were categorized as stages I-II or III-IV. Histological types were categorized as SCC or non-SCC. Age was a continuous variable. A p value < 0.05 was considered significant.

Results

Clinical characteristics and pathological data

The clinical characteristics and pathological data of 98 patients are summarized in Additional file 2: Table S2. The median age was 50.5 years (range from 34 to 73). Of 98 patients, 67 patients (68.4%) had squamous cell carcinoma (SCC), 26 patients (26.5%) had adenocarcinoma (AC), and 5 patients (5.1%) had adenosquamous carcinoma (ASC). At initial diagnosis, there were 16 (16.3%) patients in stage I, 34 (34.7%) in stage II, 31 (31.6%) in stage III, and 11 (11.2%) in stage IV. PD-L1 positivity was found in 54 (72.0%) of 75 patients. The PD-L1 expression status was unknown in 23 patients.

Landscapes of frequently mutated genes in our cohort

We performed mutation spectrum analysis in 98 cervical cancer samples, including 26 AC samples, 67 SCC samples, and 5 ASC samples. Validated gene mutations were detected in 91 of the 98 tumours (93%). The top 20 most frequently mutated genes are shown in Fig. 1A. The five genes with the highest mutation frequency in the overall samples were the PIK3CA (40%), KMT2D (26%), KMT2C (26%), LRP1B (14%), and FBXW7 (13%) genes. The gene mutations were mostly present in genes involved in the PI3K-AKT signalling pathway. Activation of PIK3CA mutations in cancer predominantly leads to the dysregulation of the PI3K/AKT/mTOR pathway (Fig. 1B).

Fig. 1
figure 1

Landscapes of frequently mutated genes in cervical cancer. A Oncoplot displaying the landscapes of frequently mutated genes. Genes are ordered according to the mutation frequency (left panel), and different mutation types are indicated by the annotation bar (bottom). B Genomic mutations of the PI3K/mTOR genes in cervical cancer. C Differences in mutations between adenocarcinoma (AC) and squamous cell carcinoma (SCC). *p < 0.05

Full size image

Differences in gene mutations between AC and SCC in our cohort

The distributions of gene mutations detected in AC and SCC are shown in Fig. 1C. We observed a higher frequency of PIK3CA gene mutation in SCC than in AC (49% vs. 15%, p = 0.004, adjust p value = 0.049). We also detected a trend towards more patients with SCC harbouring TERT gene mutations (15% vs. 0%, p = 0.057, adjust p value = 0.512), Although the difference was insignificant after multiple test adjustments.

Gene mutation differences between our cohort and the TCGA cohort

The top 20 most frequently mutated genes in the TCGA cohort are shown in Fig. 2A. We observed a relatively consistent pattern of the frequently mutated genes in the TCGA cohort compared with our cohort. Notably, a higher TERT gene mutation frequency was detected in our cohort than in the TCGA cohort (12% vs. 1%, p < 0.001), especially in SCC (15% vs. 2%, p < 0.001) (Fig. 2B, C).

Fig. 2
figure 2

Landscapes of frequently mutated genes in cervical cancer from TCGA and differences in mutations between our cohort and the TCGA cohort. A Oncoplot displaying the landscapes of frequently mutated genes from the TCGA cohort. Genes are ordered according to the mutation frequency (left panel), and different mutation types are indicated by the annotation bar (bottom). B Gene mutation differences between our cohort and the TCGA cohort. Comparison of gene mutations between our cohort and the TCGA cohort in cervical squamous cell carcinoma (SCC) (C) and cervical adenocarcinoma (AC) (D)

Full size image

We also compared gene mutations among the different histological subtypes between our cohort and the TCGA cohort. There has a different histological distribution between the TCGA cohort and our cohort. In our cohort, 67 patients (68.4%) had SCC, 26 patients (26.5%) had AC. In TCGA, 229 patients (77.1%) had SCC, 43 patients (14.5%) had AC. In cervical SCC, the frequencies of PIK3CA, KMT2D and FGFR3 gene mutations were significantly higher in our cohort than in the TCGA cohort (all p < 0.05) (Fig. 2C). In cervical AC, significantly higher frequencies of BRIP1 and WRN gene mutations, which are targets of PARP inhibitors, were detected in our cohort than in the TCGA cohort (15% vs. 0%, p = 0.017; 12% vs. 0%, p = 0.049, respectively). In AC, we observed a lower PIK3CA mutation frequency in our cohort than in the TCGA cohort (15% vs. 37%, p = 0.061). The rates of TP53 and ERBB2 mutations in our cohort were consistent with those in the TCGA cohort (Fig. 2D).

Differences in PD-L1 expression between cervical SCC and AC

Among the 98 cervical cancer samples, 75 patients were tested for PD-L1 expression, and the rate of positive PD-L1 expression was 72% (54/75) (Additional file 2: Table S2). Specimens were stratified into the following three groups (C1–3) based on the cut-off values for PD-L1 expression as defined in the Methods section: C1: CPS < 1; C2: 1 ≤ CPS < 10; and C3: CPS ≥ 10. The distribution of cervical cancer samples in each group was as follows: 28% (21/75) in C1; 35% (26/75) in C2; and 37% (28/75) in C3. The expression of PD-L1 in cervical AC and cervical SCC in the different subgroups is shown in Fig. 3A. The rate of positive PD-L1 expression in cervical SCC was significantly higher than that in cervical AC (87% vs. 39%, p < 0.001), and the expression intensity of PD-L1 in cervical SCC was significantly higher than that in cervical AC (median CPS: 4 vs. 0, p = 0.004) (Fig. 3B).

Fig. 3
figure 3

PD-L1 expression and tumour mutational burden (TMB) in cervical cancer. A Distribution of PD-L1 expression in different histological subtypes of cervical cancer. Comparison of PD-L1 expression (B) and TMB (C) between cervical squamous cell carcinoma (SCC) and cervical adenocarcinoma (AC). C1: CPS < 1; C2: 1 ≤ CPS < 10; and C3: CPS ≥ 10. (D) Associations between TMB and mutations in the KMT2C, LRP1B, PIK3CA, KMT2D, FBXW7, FAT1, LRP2, ARID1A, EP300, KMT2A, PRKDC, and SPEN genes. *p < 0.05; **p < 0.01; ***p < 0.001. WT, wild-type; MT, mutant type

Full size image

Differences in TMB between cervical SCC and AC

We defined TMB-high (TMB-H) as the top 25% of the TMB value of the samples in pan-cancer datasets. Among the 98 cervical cancer samples, 40% (39/98) were TMB-H, and 60% (59/98) were TMB-low. In comparing the difference in TMB between cervical SCC and AC, we found that SCC showed a significantly higher TMB than AC (median TMB: 5.830 vs. 1.670, p < 0.001) (Fig. 3C).

KMT2C and LRP1B gene mutations are associated with increased TMB in cervical cancer

We next explored the correlation between gene mutations and TMB in cervical cancer by analysing the top 15 most frequently mutated genes. KMT2C gene mutation in cervical cancer was associated with higher TMB levels than in cervical cancers with wild-type KMT2C (median 10 vs. 2.5, p < 0.001). Similar results were observed with LRP1B, PIK3CA, KMT2D, FBXW7, FAT1, LRP2, ARID1A, KMT2A, PRKDC, SPEN, and EP300 gene mutations (all p < 0.05) (Fig. 3D).

To validate the association of gene mutations with TMB values, logistic regression was performed to analyse the factors that influence TMB-H. We analysed the impact of histological subtype by dividing subtypes into SCC and non-SCC. The results indicated that the independent risk factors for TMB-H were KMT2C gene mutation and LRP1B gene mutation (all p < 0.05) (Table 1).

Table 1 Univariate and multivariate logistic regression of factors influencing TMB-high in cervical cancer
Full size table

Correlations of PTEN mutations and PD-L1 expression with clinical outcomes

We next analysed the correlation between genomic mutations and clinical outcomes. A total of 29 patients were excluded because gene mutations were not detected in 7 patients, 9 patients were lost to follow-up, and 13 patients received immunotherapy treatment. In 69 patients, PTEN mutations (7/69, 10.1%) were associated with poorer PFS (12.16 vs. 21.75 months, log-rank test p = 0.0024) and tended to worsen OS (log-rank test p = 0.096) compared to wild-type PTEN (Fig. 4A, B). To construct the Cox proportional hazards model, previously reported prognostic factors, such as age (continuous value), histological type (non-SCC vs. SCC), and stage, were used as adjustment factors during the analysis. In multivariate analysis, PTEN mutations were correlated with shorter PFS (hazard ratio 3.946, 95% CI, 1.500–10.380, p = 0.050) (Table 2). This suggests that PTEN mutations are an independent predictive factor of poorer clinical outcome in our patients.

Fig. 4
figure 4

Kaplan–Meier estimated A PFS and B OS by PTEN mutational status

Full size image
Table 2 Univariate and multivariate analyses of clinical parameters on progression-free survival (PFS) (Cox regression) (N = 69)
Full size table

A survival analysis was also performed in 54 patients by PD-L1 expression status. A total of 21 patients were excluded because 8 patients were lost to follow-up and 13 patients received immunotherapy treatment. There was no significant difference between PD-L1-positive and PD-L1-negative patients in terms of OS (log-rank test p = 0.1684). However, shorter PFS was observed in patients with positive PD-L1 expression (log-rank test p = 0.0267) (Additional file 3: Fig. S1).

Discussion

There are limited data on the genomic alteration profiles of cervical cancer in Chinese patients. In this study, cervical cancer samples from 98 Chinese patients were analysed using NGS and immunohistochemistry to identify the characteristic features of gene alterations as well as TMB and PD-L1 expression. Significant differences in the personal genomic landscapes were detected across cervical cancer subtypes, and our results indicated that mutations in the KMT2C and LRP1B genes were associated with higher TMB values. In addition, our findings suggest that PTEN mutations and PD-L1 expression are related to the prognosis of patients.

PD-L1 expression determined by immunohistochemistry is a validated biomarker with a strong correlation to the response to immune checkpoint inhibitors. A previous report showed that the proportion of positive PD-L1 expression (≥ 50%) in Chinese patients with non-small-cell lung cancer might be different from that in patients from Western countries [13]. In KEYNOTE 158, the overall response rate of cervical cancer patients with PD-L1-positive tumours (CPS ≥ 1) to immunotherapy was 14.6% [4]. Previous studies have indicated that the efficacy of immune checkpoint inhibitor therapies is limited in cervical cancer patients [14]. According to previous reports, PD-L1 was expressed in 34.4–96% of cervical cancer tissues, while it was rarely found in normal cervical tissues [15]. Helen and other researchers reported PD-L1 positivity in 83 of 154 (54%) SCC patients and 7 of 49 (14%) AC patients (p < 0.001) [16]. Consistent with these data, we also noticed that PD-L1 expression was more frequent in SCC (87%) than in AC (39%), and the median CPS value in SCC (4) was higher than that in AC (0). Similar to the findings of previous studies, the results of our study showed that patients who were PD-L1-negative had a better prognosis [17]. Nonetheless, the results of our prospective clinical study suggested that patients with high PD-L1 expression had a better ORR (70.4% vs. 33.3%; P = 0.041) and longer PFS (P = 0.014) than those with low expression in antivascular therapy combined with anti-PD-1 therapy (unpublished observations).Footnote 1

Importantly, our study showed that the spectrum of genomic alterations in Chinese patients with cervical cancer was roughly the same as that in patients from Western countries. In our study, the PIK3CA, KMT2C, KMT2D, LRP1B, and FBXW7 genes were the five genes with the highest mutation frequencies in cervical cancer samples, which is mostly consistent with the results in TCGA. However, we found a substantially higher TERT alteration frequency in our study than in TCGA. One reason for this result may be that the TERT mutations occurred in the promoter, and while WES is unable to detect mutations in the promoter, we were able to detect these mutations using NGS. To the best of our knowledge, this is the first study to report a TERT promoter mutation in a Chinese population with cervical cancer. By WGS data, TERT hotspot mutations were found in 74% of primary glioblastoma cases in a previous study, and these mutations resulted in increased TERT RNA expression [18, 19]. The same hotspot mutation was also found in our research by NGS. These results indicate that TERT mutations may represent a therapeutic target for cervical cancer.

We also compared the frequency of mutations among histological subgroups of cervical cancer in this study. The prognosis of AC is worse, which is probably because of the differences in genetic mutations between subgroups. Our results revealed different characteristics of gene mutations in different pathological types of cervical cancer. PIK3CA gene alterations were considerably higher in SCC (49%) than in AC (15%). The secondary analysis of CLAP trial also suggested that same result [20]. PIK3CA mutations occurred most frequently in the overall patient group, and the mutation rate in our study was higher than the rates in previous studies of cervical carcinoma from the Netherlands (20%), France (27%), Latin America (28–33%), the USA (31%) and Norway (15%) [3, 21,22,23,24]. These differences may be related to differences in race, ethnicity, geography, tumour characteristics, or staging. PIK3CA is not only one of the PI3K/AKT/mTOR pathway members but is also the most frequently mutated oncogene in human cancers [25]. In vitro studies have shown that the activated PI3K/AKT/COX-2 pathway may induce human AC HeLa cells to develop resistance to radiation [26]. Nusrat and other researchers recently reported that microsatellite stable colorectal cancer patients with PIK3CA mutations benefited from immunotherapy [27]. Another study showed that PIK3CA mutations were associated with the response to immunotherapy in cervical cancer [7]. However, only 32 patients were included in this analysis. Therefore, the relationship between PIK3CA mutations and immunotherapy needs to be further investigated in a study with a larger sample size. Our results also showed that the frequency of TERT gene mutations was significantly higher in SCC than AC.

We also reported for the first time the difference in the frequency of FGFR3 mutations between cervical SCC patients in our cohort and the TCGA database cohort. Patients with FGFR3 mutation may have a relatively worse prognosis after chemotherapy and immune checkpoint inhibition [28]. A recent clinical trial of the selective FGFR1-3 inhibitor infigratinib for the treatment of FGFR3-altered metastatic breast cancer as second-line therapy showed that the overall response rate was as high as 25% [29]. FGFR3 inhibitors may thus represent a potential therapeutic method for cervical cancer patients with FGFR3 mutations. The mutation frequency of the KMT2D gene was significantly higher in our group than in TCGA. Wang et al. suggested that KMT2D deficiency makes tumours more sensitive to immune checkpoint blockade by enhancing tumour immunogenicity [30]. Our data indicated that KMT2D mutation was also related to higher TMB in cervical cancer. Moreover, the results of a prospective study in our centre suggested that patients with KMT2D mutations had a better ORR (p < 0.05) with antivascular therapy combined with anti-PD-1 therapy.Footnote 2 These results could help identify a sizeable patient subpopulation that may show sensitivity to immune checkpoint inhibition.

We also found significantly higher BRIP1 and WRN gene mutations in AC patients in our cohort than in the TCGA cohort. The BRIP1 and WRN genes are important genes for homologous recombination repair and targets for PARP inhibitors. Thus, cervical cancers with BRIP1 and WRN alterations may be affected by PARP inhibitors.

The mutation frequencies of the KRAS, TP53 and ERBB2 genes in AC in our group were consistent with those in the TCGA cohort. Retrospective studies identified ERBB2 gene mutations in 35–6% of cervical cancers and reported that ERBB2 gene mutations may be associated with a poor prognosis [24, 31, 32]. Although these results need to be further analysed in larger sample sizes, our findings indicate that SCC and AC exhibit different molecular characteristics, which suggests that identifying specific targeted treatment strategies, such as PI3K and MEK inhibitors, may improve the clinical outcomes of these patients.

We also examined TMB in the samples and performed statistical analysis to identify gene mutations that were related to increased TMB. Previous studies have shown that a high TMB value is associated with an improved therapeutic effect of immunotherapy across cancers, including cervical cancer [33]. The TMB value could be extrapolated to the burden measured by WES, and therefore, it may be related to the predicted benefit of immune checkpoint inhibitors [34]. However, the use of WES to quantify TMB is currently not feasible in clinical practice. Rizvi et al. [35] showed that the predicted value of TMB by NGS has a good correlation with that of WES. A study verified the accuracy and predictive value of TMB evaluated by the targeted gene panel we used in the current study [36]. In our study, we calculated TMB using an NGS-based gene panel test. We found that the TMB value was associated with the cervical cancer subtype: SCC had a significantly higher TMB than AC. Our findings indicate that the overall mutational landscapes have significant differences between cervical cancer subtypes. Thus, the differences in the responses to immune checkpoint inhibitors of Chinese patients with different cervical cancer subtypes could be caused by the variance in gene alterations, PD-L1 expression and TMB status across different subtypes. Further studies using a large sample size are needed to test this hypothesis.

Interestingly, we also found that genetic mutations in KMT2C and LRP1B are independent predictors of TMB-H. To our knowledge, this is the first finding that genetic mutations in KMT2C and LRP1B are independent predictors of TMB-H in cervical cancer. KMT2C, a member of the KMT2 family, has been identified as a tumour suppressor that is frequently altered in multiple cancers [37]. Non-small-cell lung cancer patients with a mutated KMT2C gene have a higher TMB as well as PD-L1+/TMB-H, which are associated with a significantly longer median progression-free survival than those with wild-type KMT2C [38]. In our cohort, the KMT2C gene was the third most frequent gene mutation in cervical cancer and was also related to a higher TMB. These findings suggest the possibility of KMT2C gene mutation as a predictor of prognosis and response to immunotherapy and targeted therapy in cervical cancer. LRP1B, an endocytic low-density lipoprotein family receptor, is considered to be a candidate tumour suppressor that binds to extracellular ligands. Alterations in LRP1B were associated with high TMB in melanoma and non-small-cell lung cancer [39]. Although several studies have investigated the relationship between LRP1B and TMB, no studies have provided definitive findings for cervical cancer. Here, we found that mutations in LRP1B were associated with a higher TMB. LRP1B, as a result, may be defined as a predictive marker for the efficiency of CC immunotherapy.

The PTEN tumour suppressor is the second most commonly inactivated gene across cancer types. In a large cohort of non-small-cell lung cancer (NSCLC) patients, PTEN loss was associated with poorer prognosis [40]. Loss of PTEN has been associated with resistance to BRAF inhibitors and decreased overall survival in melanoma [41, 42]. Similarly, the results of this study show that PTEN mutations are associated with worse clinical outcomes. However, the secondary analysis results of the CLAP trial suggest that PTEN alterations were associated with improved outcomes in patients with cervical cancer treated with PD-1 inhibitor combination therapy, conferred longer PFS (p = 0.05) and trended towards increased OS (p = 0.08) [7]. PTEN mutations are associated with worse prognosis, but patients with PTEN mutations may benefit from anti-PD-1 combination therapy.

The study had several limitations. One limitation of this paper is the insufficient sample size. Another limitation is the lack of direct data for assessing the relationship between gene mutations and clinical prognosis. The third limitation is the heterogeneity of patient baselines. The fourth limitation is the TMB cut-off value was derived from the pan-cancer tumor. The fifth limitation is the absence of HPV status. However, it is well known that 99.7% of cervical cancers are associated with HPV infection [43], and squamous cell carcinoma and adenocarcinoma are the major pathological types of cervical cancer. HPV-independent cervical cancer including gastric type adenocarcinoma, clear cell adenocarcinoma, mesonephric adenocarcinoma and endometrioid adenocarcinoma [44]. This study including patients with squamous cell carcinoma or adenocarcinoma, and the relationship between HPV status and prognosis may not be positive. Therefore, further research is needed to investigate the connection between genetic alterations and the efficiency of immunotherapy in Chinese patients with cervical cancer.

Conclusions

In conclusion, our study revealed the genetic landscape of cervical cancer in a relatively large sample of Chinese patients. We also identified the different genomic landscapes and immune characteristics of cervical SCC and AC. These findings may provide a basis for discovering potential therapeutic targets and improving the treatment of cervical cancer patients. We also showed that cervical cancers with mutations in the KMT2C and LRP1B genes have an increased TMB, and our data indicate that KMT2C and LRP1B gene mutations may serve as biomarkers to forecast therapeutic reactions. More importantly, one critical gene, PTEN, was identified to function as a prognostic marker for cervical cancer patient survival, providing insights for the application of diagnostic and prognostic tools. Nevertheless, large-sample prospective studies are still needed to verify our conclusions.

Availability of data and materials

Our cohort datasets generated and analysed during the current study are available in the NCBI SRA repository, under accession PRJNA851600 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA851600).

Notes

  1. Qin Xu, et al. Genomic profiling and PD-L1 expression of advanced cervical cancer to predict response to programmed death-1 inhibitor combination therapy: a secondary analysis of the CIBI308ALTER-C201 trial [abstract]. Gynecologic Oncology (unpublished observations).

  2. Qin Xu, et al. Genomic profiling and PD-L1 expression of advanced cervical cancer to predict response to programmed death-1 inhibitor combination therapy: a secondary analysis of the CIBI308ALTER-C201 trial [abstract]. Gynecologic Oncology (unpublished observations).

Abbreviations

SCC:

Squamous cell carcinoma

AC:

Adenocarcinoma

TCGA:

The Cancer Genome Atlas

NGS:

Next-generation sequencing

TMB:

Tumour mutational burden

CPS:

Combined positive score

SNVs:

Single nucleotide variants

References

  1. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ, He J. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–32.

    Article  PubMed  Google Scholar 

  2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7.

    Article  PubMed  Google Scholar 

  3. The Cancer Genome Atlas Research Network. Integrated genomic and molecular characterization of cervical cancer. Nature. 2017; 543(7645):378–384.

  4. Chung HC, Ros W, Delord J-P, Perets R, Italiano A, Shapira-Frommer R, et al. Efficacy and safety of pembrolizumab in previously treated advanced cervical cancer: results from the phase II KEYNOTE-158 study. J Clin Oncol Off J Am Soc Clin Oncol. 2019;37(17):1470–8.

    Article  CAS  Google Scholar 

  5. Marabelle A, Fakih M, Lopez J, Shah M, Shapira-Frommer R, Nakagawa K, et al. Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol. 2020;21(10):1353–65.

    Article  CAS  PubMed  Google Scholar 

  6. Wang M, Fan W, Ye M, Tian C, Zhao L, Wang J, et al. Molecular profiles and tumor mutational burden analysis in Chinese patients with gynecologic cancers. Sci Rep. 2018;8(1):8990.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Huang X, He M, Peng H, Tong C, Liu Z, Zhang X, et al. Genomic profiling of advanced cervical cancer to predict response to programmed death-1 inhibitor combination therapy: a secondary analysis of the CLAP trial. J Immunother Cancer. 2021;9(5):e002223.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Galic V, Herzog TJ, Lewin SN, Neugut AI, Burke WM, Lu Y-S, Hershman DL, Wright JD. Prognostic significance of adenocarcinoma histology in women with cervical cancer. Gynecol Oncol. 2012;125(2):287–91.

    Article  PubMed  Google Scholar 

  9. Eifel PJ, Burke TW, Morris M, Smith TL. Adenocarcinoma as an independent risk factor for disease recurrence in patients with stage IB cervical carcinoma. Gynecol Oncol. 1995;59(1):38–44.

    Article  CAS  PubMed  Google Scholar 

  10. Lee Y-Y, Choi CH, Kim T-J, Lee J-W, Kim B-G, Lee J-H, Bae D-S. A comparison of pure adenocarcinoma and squamous cell carcinoma of the cervix after radical hysterectomy in stage IB-IIA. Gynecol Oncol. 2011;120(3):439–43.

    Article  PubMed  Google Scholar 

  11. Kojima T, Shah MA, Muro K, Francois E, Adenis A, Hsu C-H, et al. Randomized phase III KEYNOTE-181 study of pembrolizumab versus chemotherapy in advanced esophageal cancer. J Clin Oncol Off J Am Soc Clin Oncol. 2020;38(35):4138–48.

    Article  CAS  Google Scholar 

  12. Zhang M, Luo J, Sun Q, Harber J, Dawson A, Nakas A, et al. Author correction: clonal architecture in mesothelioma is prognostic and shapes the tumour microenvironment. Nat Commun. 2021;12(1):3569.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Aggarwal C, Abreu DR, Felip E, Carcereny E, Baas P. Prevalence of PD-L1 expression in patients with non-small cell lung cancer screened for enrollment in KEYNOTE-001, -010, and -024. Ann Oncol. 2016; 27(suppl_6).

  14. Frenel J-S, Le Tourneau C, O’Neil B, Ott PA, Piha-Paul SA, Gomez-Roca C, et al. Safety and efficacy of pembrolizumab in advanced, programmed death ligand 1-positive cervical cancer: results from the phase Ib KEYNOTE-028 trial. J Clin Oncol Off J Am Soc Clin Oncol. 2017;35(36):4035–41.

    Article  CAS  Google Scholar 

  15. Enwere EK, Kornaga EN, Dean M, Koulis TA, Phan T, Kalantarian M, et al. Expression of PD-L1 and presence of CD8-positive T cells in pre-treatment specimens of locally advanced cervical cancer. Mod Pathol Off J US Can Acad Pathol Inc. 2017;30(4):577–86.

    CAS  Google Scholar 

  16. Heeren AM, Punt S, Bleeker MC, Gaarenstroom KN, van der Velden J, Kenter GG, de Gruijl TD, Jordanova ES. Prognostic effect of different PD-L1 expression patterns in squamous cell carcinoma and adenocarcinoma of the cervix. Mod Pathol Off J Us Can Acad Pathol. 2016;29(7):753–63.

    CAS  Google Scholar 

  17. Gu X, Dong M, Liu Z, Mi Y, Yang J, Zhang Z, et al. Elevated PD-L1 expression predicts poor survival outcomes in patients with cervical cancer. Cancer Cell Int. 2019;19:146.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Wang L-B, Karpova A, Gritsenko MA, Kyle JE, Cao S, Li Y, et al. Proteogenomic and metabolomic characterization of human glioblastoma. Cancer Cell. 2021;39(4):509.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Killela PJ, Reitman ZJ, Jiao Y, Bettegowda C, Agrawal N, Diaz LA, et al. TERT promoter mutations occur frequently in gliomas and a subset of tumors derived from cells with low rates of self-renewal. Proc Natl Acad Sci USA. 2013;110(15):6021–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Huang X, He M, Peng H, Tong C, Liu Z, Zhang X, et al. Genomic profiling of advanced cervical cancer to predict response to programmed death-1 inhibitor combination therapy: a secondary analysis of the CLAP trial. J Immunother Cancer. 2021;9:e002223.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Spaans VM, Trietsch MD, Peters AAW, Osse M, Ter Haar N, Fleuren GJ, Jordanova ES. Precise classification of cervical carcinomas combined with somatic mutation profiling contributes to predicting disease outcome. PLoS ONE. 2015;10(7):e0133670.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Wright AA, Howitt BE, Myers AP, Dahlberg SE, Palescandolo E, Van Hummelen P, et al. Oncogenic mutations in cervical cancer: genomic differences between adenocarcinomas and squamous cell carcinomas of the cervix. Cancer. 2013;119(21):3776–83.

    Article  CAS  PubMed  Google Scholar 

  23. Lou H, Villagran G, Boland JF, Im KM, Polo S, Zhou W, et al. Genome analysis of Latin American cervical cancer: frequent activation of the PIK3CA pathway. Clin Cancer Res Off J Am Assoc Cancer Res. 2015;21(23):5360–70.

    Article  CAS  Google Scholar 

  24. Ojesina AI, Lichtenstein L, Freeman SS, Pedamallu CS, Imaz-Rosshandler I, Pugh TJ, et al. Landscape of genomic alterations in cervical carcinomas. Nature. 2014;506(7488):371–5.

    Article  CAS  PubMed  Google Scholar 

  25. German S, Aslam HM, Saleem S, Raees A, Anum T, Alvi AA, Haseeb A. Carcinogenesis of PIK3CA. Hered Cancer Clin Pract. 2013;11(1):5.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Xia S, Zhao Y, Yu S, Zhang M. Activated PI3K/Akt/COX-2 pathway induces resistance to radiation in human cervical cancer HeLa cells. Cancer Biother Radiopharm. 2010;25(3):317–23.

    Article  CAS  PubMed  Google Scholar 

  27. Nusrat M, Roszik J, Katkhuda R, Menter D, Kopetz S. Association of PIK3CA mutations (mut) with immune engagement and clinical benefit from immunotherapy in microsatellite stable (MSS) colorectal cancer (CRC) patients (pts). J Clin Oncol. 2019;37(15_suppl):3604–3604.

    Article  Google Scholar 

  28. Robertson AG, Kim J, Al-Ahmadie H, Bellmunt J, Guo G, Cherniack AD, et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell. 2018;174(4):1033.

    Article  CAS  PubMed  Google Scholar 

  29. Pal SK, Rosenberg JE, Hoffman-Censits JH, Berger R, Quinn DI, Galsky MD, et al. Efficacy of BGJ398, a fibroblast growth factor receptor 1–3 inhibitor, in patients with previously treated advanced urothelial carcinoma with alterations. Cancer Discov. 2018;8(7):812–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wang G, Chow RD, Zhu L, Bai Z, Ye L, Zhang F, et al. CRISPR-GEMM pooled mutagenic screening identifies KMT2D as a major modulator of immune checkpoint blockade. Cancer Discov. 2020;10(12):1912–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Oaknin A, Friedman CF, Roman LD, D’Souza A, Brana I, Bidard F-C, et al. Neratinib in patients with HER2-mutant, metastatic cervical cancer: findings from the phase 2 SUMMIT basket trial. Gynecol Oncol. 2020;159(1):150–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Xiang L, Jiang W, Ye S, He T, Pei X, Li J, et al. ERBB2 mutation: a promising target in non-squamous cervical cancer. Gynecol Oncol. 2018;148(2):311–6.

    Article  CAS  PubMed  Google Scholar 

  33. Yarchoan M, Hopkins A, Jaffee EM. Tumor mutational burden and response rate to PD-1 inhibition. N Engl J Med. 2017;377(25):2500–1.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Campesato LF, Barroso-Sousa R, Jimenez L, Correa BR, Sabbaga J, Hoff PM, Reis LFL, Galante PAF, Camargo AA. Comprehensive cancer-gene panels can be used to estimate mutational load and predict clinical benefit to PD-1 blockade in clinical practice. Oncotarget. 2015;6(33):34221–7.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Rizvi H, Sanchez-Vega F, La K, Chatila W, Jonsson P, Halpenny D, et al. Molecular determinants of response to anti-programmed cell death (PD)-1 and anti-programmed death-ligand 1 (PD-L1) blockade in patients with non-small-cell lung cancer profiled with targeted next-generation sequencing. J Clin Oncol Off J Am Soc Clin Oncol. 2018;36(7):633–41.

    Article  CAS  Google Scholar 

  36. Fang W, Ma Y, Yin JC, Hong S, Zhou H, Wang A, et al. Comprehensive genomic profiling identifies novel genetic predictors of response to anti-PD-(L)1 therapies in non-small cell lung cancer. Clin Cancer Res Off J Am Assoc Cancer Res. 2019;25(16):5015–26.

    Article  CAS  Google Scholar 

  37. Yang B, Li J, Li F, Zhou H, Shi W, Shi H, et al. Comprehensive analysis of age-related somatic mutation profiles in Chinese young lung adenocarcinoma patients. Cancer Med. 2019;8(4):1350–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Shi Y, Lei Y, Liu L, Zhang S, Wang W, Zhao J, et al. Integration of comprehensive genomic profiling, tumor mutational burden, and PD-L1 expression to identify novel biomarkers of immunotherapy in non-small cell lung cancer. Cancer Med. 2021;10(7):2216–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Chen H, Chong W, Wu Q, Yao Y, Mao M, Wang X. Association of mutation with tumor mutation burden and outcomes in melanoma and non-small cell lung cancer patients treated with immune check-point blockades. Front Immunol. 2019;10:1113.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kerr KM, Dafni U, Schulze K, Thunnissen E, Bubendorf L, Hager H, et al. Prevalence and clinical association of gene mutations through multiplex mutation testing in patients with NSCLC: results from the ETOP Lungscape Project. Ann Oncol Off J Eur Soc Med Oncol. 2018;29(1):200–8.

    Article  CAS  Google Scholar 

  41. Paraiso KHT, Xiang Y, Rebecca VW, Abel EV, Chen YA, Munko AC, et al. PTEN loss confers BRAF inhibitor resistance to melanoma cells through the suppression of BIM expression. Can Res. 2011;71(7):2750–60.

    Article  CAS  Google Scholar 

  42. Bucheit AD, Chen G, Siroy A, Tetzlaff M, Broaddus R, Milton D, et al. Complete loss of PTEN protein expression correlates with shorter time to brain metastasis and survival in stage IIIB/C melanoma patients with BRAFV600 mutations. Clin Cancer Res Off J Am Assoc Cancer Res. 2014;20(21):5527–36.

    Article  CAS  Google Scholar 

  43. Walboomers JM, Jacobs MV, Manos MM, Bosch FX, Kummer JA, Shah KV, et al. Human papillomavirus is a necessary cause of invasive cervical cancer worldwide. J Pathol. 1999;189(1):12–9.

    Article  CAS  PubMed  Google Scholar 

  44. WHO Classification of Tumours Editorial Board. Female genital tumours. In: WHO classification of tumours: female genital tumours. 5th ed. Lyon: International Agency for Research on Cancer; 2020. p. 631.

Download references

Acknowledgements

We appreciate all subjects who participated in this study.

Funding

This study was supported by the Joint Funds for the Innovation of Science and Technology Program of Fujian Province, China, No. 2018Y9110, and the Natural Science Foundation of Fujian Province, China, No. 2020J011126.

Author information

Author notes

  1. Jing Liu, Zirong Li, Ting Lu and Junping Pan contributed equally to this work

Authors and Affiliations

  1. Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420 Fuma Road, Jinan District, Fuzhou, 350014, China

    Jing Liu, Ying Chen & Qin Xu

  2. Department of Radiation Oncology, Longyan First Hospital, Affiliated to Fujian Medical University, Longyan, Fujian, China

    Zirong Li

  3. Department of Pathology, Fujian Women and Children Hospital, Fuzhou, Fujian, China

    Ting Lu

  4. Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420 Fuma Road, Jinan District, Fuzhou, 350014, China

    Junping Pan, Li Li & Yanwen Song

  5. Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420 Fuma Road, Jinan District, Fuzhou, 350014, China

    Dan Hu

  6. Department of Radiotherapy, Zhangzhou Hospital, Teaching Hospital of Fujian Medical University, Zhangzhou, 363000, Fujian, China

    Yanhong Zhuo

Authors
  1. Jing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zirong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ting Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junping Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Li Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yanwen Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dan Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yanhong Zhuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ying Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Qin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

QX and JL contributed to the conception and design of the study. ZL, TL and JP contributed to the data collection. ZL performed the statistical analysis of the data. All authors contributed to the article and approved the submitted version. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Qin Xu.

Ethics declarations

Ethics approval and consent to participate

This study and its protocols were approved by the Institutional Review Board of Fujian Provincial Cancer Hospital (Fuzhou, China), and all subjects signed informed consent forms (SQ2020-096-01). All patients gave informed consent for molecular analysis of blood and tissue. All analyses were conducted in accordance with the tenets of the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests, and all authors confirm the accuracy of the findings.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1

. Gene mutations in the TCGA cohort.

Additional file 2: Table S2

. Clinicopathological features of cervical cancer patients.

Additional file 3: Figure S1

. Kaplan-Meier estimated A PFS and B OS by PD-L1 expression.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, J., Li, Z., Lu, T. et al. Genomic landscape, immune characteristics and prognostic mutation signature of cervical cancer in China. BMC Med Genomics 15, 231 (2022). https://doi.org/10.1186/s12920-022-01376-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12920-022-01376-9

Keywords

BMC Medical Genomics

ISSN: 1755-8794

Contact us